Research on Partner Selection of High-Speed Railway Dynamic Logistics Alliance Based on the Dynamic Programming Model
Bibliographic record
Abstract
Traditional railway transportation can no longer meet people’s demand for logistics services. This paper takes advantage of high speed to propose a high-speed railway dynamic logistics alliance based on a cloud platform to make up for the lack of transport capacity at both ends of the high-speed railway logistics trunk line. Selecting partners is crucial to the high-speed rail logistics alliance. This paper uses the methods of multiobjective fuzzy optimization and dynamic programming to conduct multistage optimization of high-speed railway dynamic logistics alliance partners. When the market demand changes, in order to optimize the overall interests of the alliance, this paper uses the efficiency profit field method to achieve the dynamic selection of alliance partners or potential partners. The case study shows that the establishment of the high-speed railway dynamic logistics alliance can optimize the interests of the members of the alliance, verify the effectiveness of the method, and provide a reference for the better development of high-speed railway logistics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".